Techniques for efficiently acquiring data

Abstract

To enable efficient acquisition of digital representation of personality data of a user by a client device from a server.SOLUTION: Digital representation of the personality data is processed at a client device (406) to provide a user-adapted service to a user (402). A method implementation of the technique is performed by a server (404), and comprises storing a neural network being trained to compute personality data of the user (402) based on input obtained from the user (402), receiving, from the client device (406), a request for digital representation of personality data for the user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user (402) is computed using a neural network (602) based on the input obtained from the user.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present disclosure generally relates to the field of data retrieval, and in particular, presents techniques for enabling the efficient retrieval by a client device of a digital representation of a user's personality data from a server. This technique can be implemented as a method, a computer program, an apparatus, and a system.

Background Art

[0002] Personality tests have been used for decades to evaluate human personality traits. Generally, they are conducted based on personality survey data obtained from the test subjects, and the survey data is evaluated by experts such as psychologists to draw conclusions about human personality. The so-called "OCEAN" model is a widely accepted classification (taxonomy) of personality traits, also known as the "Big Five" personality traits. As personality dimensions, it includes openness, conscientiousness, extraversion, agreeableness, and neuroticism. Widely known personality tests that utilize the OCEAN model include those based on the so-called International Personality Item Pool (IPIP), HEXACO-60 inventory, and Big-Five-Inventory-10 (BFI-10), and for example, include question sets for testing people for each of the five personality dimensions. In conventional personality tests, generally, a review by an expert on humans, such as a psychologist, is required to obtain a qualified evaluation of human personality traits, but it is difficult to integrate the execution of the personality test and its results into the processes executed on a technical system. Such integration can be adjusted to make the process more conform to the user's personality, and for example, can improve the user experience, such as providing the user with user-adapted services, so it can be beneficial. Summary of the Invention

[0003] Therefore, a technical implementation that actually enables the integration of a personality test and its results into the processes executed on a technical system is required.

[0004] According to a first aspect, a method is provided that enables a digital representation of a user's personality data to be efficiently obtained from a server by a client device, where the digital representation of the personality data is processed in the client device to provide a user-adapted service to the user. The method includes storing a neural network trained by a server to calculate the user's personality data based on input obtained from the user, receiving a request for a digital representation of the user's personality data from the client device, and sending the requested digital representation of the user's personality data to the client device, where the user's personality data is calculated using the neural network based on input obtained from the user.

[0005] By storing the trained neural network on a server and applying it to the calculation of the user's personality data, (eliminating the need for conventional human review) the acquisition of the digital representation of the user's personality data is automated, making it possible to integrate the acquisition and use of the user's personality data into a process (e.g., automated) executed on a technical system. In particular, the neural network can be said to be an efficient functional data structure, capable of calculating the required personality data in a single computational execution, i.e., by inputting the input obtained from the user at the input nodes of the neural network and reading the output value of the result representing the personality data from the output nodes of the neural network. In this way, the neural network enables the efficient provision of personality data in digital representation form to the client device and can be used to provide services tailored to the specific personality of the user, thereby improving the user experience on the client device side. The efficient provision of data enables the digital representation of personality data to be provided to the client device without significant delay and to be processed immediately on the client device, making the integration of the acquisition and use of personality data particularly practical. This achieves a technically feasible implementation that can actually integrate the acquisition and use of personality data into a process executed on a technical system in general.

[0006] Since the user's personality data can indicate the user's psychological characteristics and / or preferences, personality data generally includes psychological data such as classical personality data based on personality dimensions of openness, honesty, extraversion, agreeableness, and neuroticism (known as the Big Five as described above), as well as medical data (e.g., data indicating tendencies such as curiosity, anxiety, and depression). The digital representation of the user's personality data can include, for example, the digital representation of the above-described characteristics such as at least one digital representation of the personality dimensions of openness, honesty, extraversion, agreeableness, and neuroticism calculated by a neural network for the user.

[0007] A client device may be configured to process a digital representation of personality data for the purpose of enabling the provision of user-adapted services to a user. In one variant, the client device itself may be configurable based on the digital representation of personality data. Exemplary devices that may be configurable by the digital representation of personality data can be, for example, a vehicle, in which case the vehicle can be the client device. The vehicle processes the received digital representation of the personality data of a user (e.g., the driver of the vehicle) and adapts the driving settings of the vehicle to the personality of the driver, so that it can set itself (e.g., including its sub-components) to provide a driving service adapted to the personality of the user. If the personality data indicates that the driver tends to avoid risks or is anxious, for example, the driving settings of the vehicle are set to be more safety-oriented, while in the case of a driver who tends to have a more risk-seeking personality, the vehicle can be set so that the driving settings become more sporty. For this purpose, among other settings, the fuel and brake reaction behavior of the vehicle can be adapted accordingly. Sub-components of the vehicle that provide vehicle-related services, such as the sound system of the vehicle including sound and volume settings, may also be set based on the personality data to further adapt to the personality of the user.

[0008] In another variant, the client device may set at least one other device based on the digital representation of the personality data, for example, if it is at least one other device that provides services to the user. In such a variant, the client device can be, for example, a mobile terminal (e.g., a smartphone), and can interact with a vehicle (i.e., in this case, the vehicle corresponds to at least one other device) (e.g., using Bluetooth (registered trademark)). When receiving the digital representation of the personality data from the server, the mobile terminal can set the vehicle via the interface. Thus, it can be said that the digital representation of the user's personality data can be processed in the client device to set at least one device that provides services to the user. Setting at least one device may include configuring at least one setting of at least one device and / or configuring at least one setting of the services provided by at least one device. The vehicle is merely an example of a device that can be set based on personality data, and it will be understood that the client device and / or at least one other device can also correspond to other types of devices.

[0009] In one embodiment, the method executed by the server can further include receiving feedback characterizing a user, updating a neural network based on the feedback, and transmitting a digital representation of the user's updated personality data to a client device, where the user's updated personality data is calculated using the updated neural network. The digital representation of the user's updated personality data can be processed at the client device to improve the settings of at least one device that provides services to the user (e.g., one of the vehicle settings described above). The feedback can be collected at the client device and / or at at least one device that provides services to the user and can indicate the user's personality. The feedback can include, for example, behavioral data reflecting the user's behavior monitored at at least one device when using the services provided by at least one device. In one variation, the behavioral data can be monitored by at least one device that provides services to the user using (e.g., sensor-based) measurements. In the example of a vehicle, the user's behavior to be monitored can be, for example, the user's driving behavior, and the driving behavior is measured by the vehicle's sensors. To measure the driving behavior, the sensors can, for example, detect the user's braking reaction and intensity, and such measurements can indicate the user's personality (e.g., enthusiasm for driving), so this information is transmitted as feedback to the server to update the neural network, thereby improving the function of the neural network that calculates the user's personality data.

[0010] Updating the neural network can include training the neural network based on feedback received from the client device. When the feedback represents new input values that have not yet been input into the neural network, new input nodes can be added to the neural network and the new input values can be assigned to the new input nodes when training the neural network. This makes particularly clear the ability of the neural network as an efficient functional data structure employed in the technical implementation presented herein. That is, the neural network represents an efficiently updatable data structure and is updated based on any feedback regarding the user's personality received from the client device, improving the function of calculating personality data. The information conveyed by the feedback can be directly integrated into the neural network and, once trained, is immediately reflected in subsequent requests after being sent to a server that requests a digital representation of the personality data. Conventional personality assessment methods are fairly fixed and may not support such updatability at all.

[0011] The digital representation of a user's personality sent from a server to a client device can correspond to a previously computed digital representation of the user's personality by the server in response to a previous request for computation of the user's personality (e.g., when the user performs a personality test by answering a set of questions). Thus, the user's personality data can be computed before receiving a request from the client device, and the request can include an access code previously provided to the user by the server when computing the user's personality data, and the access code enables the user to access the digital representation of the user's personality data from other client devices. Such an implementation can save computational resources at the server because it is not necessary to newly compute the digital representation of the user's personality each time the digital representation of that particular user's personality data is requested from the client device, and it can also return based on the pre-computed personality data. And the user can use the access code to access the digital representation of the personality data from multiple other client devices, such as other vehicles the user can drive, e.g., cars and motorcycles, or other types of devices.

[0012] The input obtained from the user can correspond to a digital score that reflects the answer to a question regarding at least one of the user's personality, goals, and motivations (e.g., obtained in a question - answering scheme in a personality - test approach), and each of the digital scores can be used as an input to a separate input node of a neural network when calculating the user's personality data using the neural network. The digital score can correspond to, for example, a 5 - level Likert scale with values from 1 to 5. The neural network can correspond to a deep neural network having at least two hidden layers between an input layer including the input nodes of the neural network and an output layer including the output nodes. Questions regarding personality can correspond to, for example, questions from the conventional IPIP, HEXACO - 60, and / or BFI - 10 pools, but it will be understood that other questions regarding the user's personality, including questions about the user's psychological characteristics and / or preferences, can be used as well. In particular, questions regarding the user's goals and motivations can define additional dimensions (e.g., in addition to the Big Five) that enhance the accuracy of the calculated personality data compared to the conventional IPIP, HEXACO - 60, and BFI - 10 approaches. The network can be trained based on data collected in a baseline survey conducted on a plurality of testers (e.g., 1000 or more), and the baseline survey can be conducted using the questions described above.

[0013] To reduce the computational complexity when calculating the user's personality data, the neural network can be designed to have a specific network structure. Considering the context of the above questions, the structure of the neural network can generally be designed such that the number of input nodes decreases compared to the number of input nodes available when all of the above questions are used. Therefore, the questions can correspond to questions selected from a set of questions that represent the results that can be optimally achieved in calculating the user's personality data (i.e., when the user answers all the questions in the set of questions), where the selected questions can correspond to the questions in the set of questions determined to be the most influential with respect to the optimally achievable results. As described above, since each answer to a question is input into an individual input node of the neural network, selecting a subset of the set of questions reduces the number of input nodes when calculating the personality data and reduces the computational complexity. Due to the fact that the questions that are the most influential with respect to the achievable results are selected, the accuracy of the results output by the neural network is approximately maintained.

[0014] In fact, tests have shown that the number of questions can be significantly reduced without significantly sacrificing the accuracy of the results. Taking as a set of questions that represent the results that can be optimally achieved in calculating the personality data, a set of questions including the standard IPIP, HEXACO-60, and BFI-10 questions (a total of 370 questions), and optionally supplemented by additional questions regarding the user's goals and motivations (resulting in a total number of questions exceeding 370), tests have shown that when only the 30 most influential questions are used, approximately 90% of the accuracy of the optimally achievable results is achieved. Therefore, the number of questions selected can be less than 10% (preferably less than 5%) of the number of questions included in the set of questions that represent the optimally achievable results. In this case, since the number of input nodes of the neural network can be significantly reduced, computational resources are significantly saved and the personality data can be calculated more efficiently.

[0015] In one variation, to determine the questions of the most influential set of questions regarding the optimally achievable result, the result achievable by each single question of the set of questions is correlated with the optimally achievable result, and questions are selected from the set of questions based on selecting the questions from the set of questions that have the highest correlation with the optimally achievable result. Thus, a fixed subset of the set of questions representing the optimally achievable result can be determined and used to reduce the number of input nodes and train the neural network as described above.

[0016] As described above, the optimally achievable result can correspond to the result achieved when the user answers all questions in a set of questions, optionally including questions from standard IPIP, HEXACO-60, and BFI-10, supplemented with additional questions regarding the user's goals and motivations as described above. On the other hand, in one variation, the standard IPIP score (obtained by answering all questions of the standard IPIP test), the standard HEXACO-60 score (obtained by answering all questions of the standard HEXACO-60 test), and the score of the standard BFI-10 (obtained by answering all questions of the standard BFI-10 test) can be obtained individually as references for the optimally achievable result, and in other variations, an improved result can be achieved by calculating a combined score of these individual scores as a reference for the optimally achievable result, where the combined score is calculated, for example, as the (e.g., weighted) average of the individual scores. The combined score can also be represented as a "super score" representing the "truth" derivable from the individual scores, generally improving the meaning of the determined scores and the reference for the optimally achievable result.

[0017] In another variation, the questions can be repeatedly selected from a set of questions, and in each of the repetitions, the next question can be selected according to the user's answer to the previous question. In each of the repetitions, the next question can be selected as one of the questions in the set of questions that is determined to have the most impact on the achievable results for calculating the user's personality data. This can be regarded as an adaptive selection of questions, and the questions are determined for each user in a step-by-step manner considering the user's answers to previous questions. In one particular variation, the neural network can include a plurality of output nodes representing the probability curve of the results of the user's personality data. Here, determining the most influential question in the set of questions as the next question in each repetition can include determining the degree to which the change in the digital score input to each of the input nodes of the neural network changes the probability curve for each of the input nodes of the neural network. The question associated with the input node determined to have the highest degree of change in the probability curve can be selected as the most influential question in each repetition.

[0018] To further reduce the computational complexity, the above-described iterative and adaptive selection can be performed under at least one constraint such as the maximum number of questions to be selected, the minimum result accuracy to be achieved (the result accuracy improves with the answer to the question in each repetition and the calculation can be stopped when the required minimum result accuracy is reached), and the maximum available time (the test can be stopped when the maximum available time has elapsed, or each question can be associated with the estimated time for the user's answer, and the number of questions to be selected can be determined based on the estimated time). These constraints can be set individually for each calculation of the personality data.

[0019] According to a second aspect, a method is provided that enables a digital representation of a user's personality data to be efficiently obtained from a server by a client device. The method is executed by the client device and includes sending a request for a digital representation of the user's personality data to the server and receiving the requested digital representation of the user's personality data from the server, wherein the user's personality data is calculated using a neural network trained to calculate the user's personality data based on input obtained from the user, and may include processing the digital representation of the personality data to provide a user-tailored service to the user.

[0020] The method according to the second aspect defines a method from the perspective of a client device that can complement the method executed by the server according to the first aspect. The server and client device of the second aspect may correspond to the server and client device described above in relation to the first aspect. Accordingly, those aspects described in relation to the method of the first aspect that are applicable to the method of the second aspect are also included by the method of the second aspect, and vice versa. Accordingly, unnecessary repetition will be omitted hereinafter.

[0021] Similar to the method of the first aspect, the digital representation of the user's personality data can be processed on the client device to configure at least one device that provides services to the user, where the at least one device can include the client device. The method executed by the client device can further include sending feedback characterizing the user to the server and receiving the digital representation of the updated personality data of the user from the server, where the updated personality data of the user can be calculated using a neural network updated based on the feedback. The digital representation of the updated personality data of the user can be processed on the client device to improve the configuration of at least one device that provides services to the user. The feedback can include behavioral data reflecting the user's behavior monitored on at least one device when using the services provided by the at least one device, where the behavioral data can be monitored using measurements executed on at least one device that provides services to the user. The at least one device can include a vehicle, and the behavioral data can include data reflecting the user's driving behavior. The user's personality data can be calculated before sending a request to the server, and the request can include an access code previously provided to the user by the server when calculating the user's personality data, and the access code enables the user to access the digital representation of the user's personality data from other client devices. The input obtained from the user can correspond to a digital score reflecting the answer to a question regarding at least one of the user's personality, goals, and motivations.

[0022] According to a third aspect, a computer program product is provided. The computer program product includes a program code portion for performing at least one of the methods of the first aspect and the second aspect when the computer program product is executed on one or more computing devices (e.g., a processor or a distributed set of processors). The computer program product can be stored on a computer-readable recording medium such as a semiconductor memory, a DVD, a CD-ROM, etc.

[0023] According to a fourth aspect, a server is provided that enables a client device to efficiently obtain a digital representation of a user's personality data from the server, where the digital representation of the personality data is processed in the client device to provide a user-adapted service to the user. The server includes at least one processor and at least one memory, and the at least one memory includes instructions executable by the at least one processor such that the server is operable to perform any of the method steps presented herein with respect to the first aspect.

[0024] According to a fifth aspect, a client device is provided for enabling efficient acquisition of a digital representation of a user's personality data from a server. The client device includes at least one processor and at least one memory, and the at least one memory includes instructions executable by the at least one processor such that the client device is operable to perform any of the method steps presented herein with respect to the second aspect.

[0025] According to a sixth aspect, a system is provided that includes a server according to the fourth aspect and at least one client device according to the fifth aspect.

[0026] Further details and advantages of the technology presented herein are described with reference to the exemplary implementations shown in the following figures.

Brief Description of the Drawings

[0027]

Figure 1a

Figure 1b

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6a

Figure 6b

Figure 7

Figure 8

[0028] In the following, specific details are described for the purpose of illustration rather than limitation to provide a thorough understanding of the present disclosure. It will be apparent to those skilled in the art that the present disclosure may be implemented by other implementations departing from these specific details.

[0029] One of ordinary skill in the art will further understand that the steps, services, and functions described herein below can be implemented using individual hardware circuits, software that operates in conjunction with a programmed microprocessor or a general purpose computer, one or more application specific integrated circuits (ASICs), and / or one or more digital signal processors (DSP). When the present disclosure is described with respect to a method, it may also be embodied in one or more processors and one or more memories coupled to the one or more processors, where the one or more memories may be encoded with one or more programs that execute the steps, services, and functions presented herein when executed by the one or more processors.

[0030] FIG. 1a schematically shows an exemplary configuration of server 100 that enables a client device to efficiently obtain a digital representation of a user's personality data from server 100, where the digital representation of the personality data is processed at the client device to provide user-adapted services to the user. Server 100 includes at least one processor 102 and at least one memory 104, and the at least one memory 104 includes instructions executable by the at least one processor 102 such that server 100 operates to execute the steps of the methods described herein with reference to the "server" herein.

[0031] It will be understood that server 100 may be implemented on a physical computing unit or on a virtualized computing unit, such as a virtual machine. Further, server 100 is not necessarily limited to being implemented on a stand-alone computing unit, and may also be implemented as a component that exists on multiple distributed computing units, realized in software and / or hardware, such as in a cloud computing environment and the like.

[0032] FIG. 1b schematically shows an exemplary configuration of a client device 110 that enables a digital representation of a user's personality data to be efficiently obtained from a server by the client device 110. The client device 110 has at least one processor 112 and at least one memory 114, and the at least one memory 114 includes instructions executable by the at least one processor 112 such that the requesting client device 110 is operable to perform the steps of the methods described herein with reference to the "client device".

[0033] FIG. 2 shows a method that can be executed by a server 100 according to the present disclosure. This method is specialized for enabling a digital representation of a user's personality data to be efficiently obtained from the server 100 by a client device (e.g., client device 110). In this method, the server 100 can perform the steps described herein with reference to the "server". Along the above description, in step S202, the server 100 can store a neural network trained to calculate a user's personality data based on an input obtained from the user. In step S204, the server 100 can receive a request for a digital representation of the user's personality data from the client device. In step S206, the server 100 can send the requested digital representation of the user's personality data to the client device, where the user's personality data is calculated using the neural network based on an input obtained from the user.

[0034] FIG. 3 shows a method that can be executed by the client device 110 according to the present disclosure. This method is specialized in enabling the digital representation of the user's personality data to be efficiently obtained by the client device 110 from a server (e.g., server 100). In this method, the client device 110 can execute the steps described herein with reference to the "client device". Along the above description, in step S302, the client device 110 can send a request for the digital representation of the user's personality data to the server. In step S304, the client device 110 can receive the requested digital representation of the user's personality data from the server. Here, the user's personality data is calculated using a neural network trained to calculate the user's personality data based on the input obtained from the user. In step S306, the client device 110 can process the digital representation of the personality data to provide a user-adapted service to the user.

[0035] Figure 4 shows an exemplary interaction between user 402, a server 404 that stores a neural network trained to compute the user's personality data based on input obtained from the user, and a client device that obtains a digital representation of the user 402's personality data and provides a user-tailored service to user 402. In the example shown here, the client device can be a vehicle 406 driven by user 402. As shown in the figure, user 402 can perform an automated personality test by answering questions using, for example, a web interface or an app on his laptop or smartphone, and provide input to the neural network stored on server 404. Based on this, the neural network can compute the personality data of user 402. Instead of sending the digital representation of the personality data to user 402, in the figure shown, server 404 can provide user 402 with an access code that can be used by user 402 to access the personality data using other client devices including vehicle 406. User 402 can use the access code to register or log in to vehicle 406 (more specifically, its board computer), and vehicle 406 can use the access code to request from server 404 the digital representation of the user's personality data (in the figure, the user's personality data is denoted as the user's "MindDNA").

[0036] When the server 404 receives a request from the vehicle 406, it can reply with the user's personality data to the vehicle 406, and the vehicle 406 can set its driving settings (and, optionally, the sub-components of the vehicle 406) according to the personality data of the user 402. For example, the vehicle 406 can adapt the fuel and brake response behaviors of the vehicle 406 to provide a driving experience that is particularly suitable for the user's personality (e.g., risk avoidance, risk seeking, etc.). When the user 402 drives the vehicle 406, the vehicle 406 can monitor the user's driving behavior, for example, by using sensors that measure the user's brake response and strength. The vehicle 406 can provide this information as feedback to the server 404, where the feedback is processed to update (by training) a neural network to improve the ability to calculate the personality data of the user 402. In response, the server 404 can send the correspondingly updated personality data of the user 402 to the vehicle 406, and the vehicle 406 can use the digital representation of the updated personality data to improve the vehicle settings to be more in line with the actual personality of the user 402. In summary, a system is provided that integrates the acquisition and use of the user's personality data into an automated process, adapts the settings of the provided device or service according to the user's preferences obtained from the user's personality data, thereby improving the user experience.

[0037] Figure 5 shows another connection option among the mobile terminal 502 (e.g., a smartphone) of user 402, vehicle 406, and server 404 according to the present disclosure. In one variant, vehicle 406 can communicate directly with server 404 via the Internet. When user 402 is authenticated in vehicle 406 (e.g., using a key, smart card, NFC / RFID, a smartphone with NFC, fingerprint, etc.), vehicle 406 can request the user's personality data (again denoted as the user's "MindDNA" in Figure 5) to improve the user's driving experience. In another variant, when user 402 is carrying mobile terminal 502, mobile terminal 502 can communicate with server 404 via the Internet (e.g., using a dedicated app installed therein) and request the user's personality data. In this variant, vehicle 406 can communicate locally with mobile terminal 502 (e.g., using Bluetooth (R), Wi-Fi, or a USB cable) and obtain the user's personality data from mobile terminal 502. The direct connection between vehicle 406 and mobile terminal 502 can be further used to utilize sensors installed in mobile terminal 502 (e.g., a gyroscope for movement and acceleration detection, a GPS for movement and acceleration detection as well as driving route detection, or a medical sensor for measuring pulse, blood pressure, etc.), supplement the feedback collected by vehicle 406 itself (e.g., related to the user's driving behavior), thereby providing additional feedback detected by mobile terminal 502 to server 404, and as described above, the neural network can be updated based on the feedback.

[0038] FIG. 6a shows an exemplary structure of a neural network 602 according to the present disclosure. The neural network 602 includes an input layer, an output layer, and two hidden layers. The neural network 602 shown in FIG. 6a merely shows the structure of a general deep neural network, and the actual number of nodes (at least in the input layer and the hidden layers) of the neural network 602 stored in the server 404 can be significantly higher than that shown. As described above, the test is performed using the 30 most influential questions out of a total of 370 or more questions (obtained from the questions of the standard IPIP, HEXACO-60, and BFI-10, and optionally supplemented by additional questions regarding goals and motivations), resulting in 30 input nodes in the input layer of the neural network 602. In this case, for example, each hidden layer can be composed of 50 nodes. Further, as shown, the neural network 602 may include a single output node in the output layer. In this case, the resulting value of the output node in the output layer may represent the value of one of the personality dimensions (of the Big Five) for which the neural network 602 was trained. Such a structure of the neural network 602 is merely exemplary, and it will be understood that other structures are generally conceivable.

[0039] The more advanced structure of the neural network 602 has input nodes corresponding to the number of the complete set of available questions, where the questions can be obtained from the standard IPIP, HEXACO-60, and BFI-10 questions, and can further include additional questions regarding the user's goals and motivations, as well as other psychological characteristics and / or preferences of the user that are not covered by the above questions, potentially adding hundreds of questions, for example, more than 600 questions. Thus, such a neural network 602 can have more than 600 input nodes each corresponding to a single question of the complete set of available questions, and the number of nodes in the hidden layer can be selected according to the performance of the neural network 602. For example, the neural network 602 can be composed of two hidden layers each having 100 nodes. Further, in the input layer, the above more than 600 input nodes can be duplicated, and each of the duplicated input nodes can be used as a missing-question-indicator. The missing-question-indicator can be dichotomous, that is, it can have two values (for example, 0 and 1) indicating whether the question of the corresponding (original) input node has been answered. Since the input nodes are duplicated, the input layer can have more than 1200 input nodes in total.

[0040] The output layer of the more advanced neural network 602 can have a plurality of output nodes that together represent the probability curve of one personality dimension. For example, if the scale used for the output of this personality dimension ranges from 0 to 10 and the number of output nodes is 50, each of the output nodes can represent a part of the scale, that is, the scale parts corresponding to 0 - 0.2, 0.2 - 0.4, 0.4 - 0.6, ··· 9.8 - 10 of the scale. Such an output layer can provide the entire probability curve of the output value of this personality dimension instead of a single output value. FIG. 6b shows an exemplary output layer together with the corresponding probability curve 604. Such a curve enables determination of where the mode of the output value (i.e., indicated by the peak of the curve) is, and also enables determination of the accuracy (i.e., indicated by the width of the curve) with which the neural network 602 calculates the result. Using the more advanced neural network 602, by training the neural network 602 separately for each dimension, the user's personality data can be calculated in the form of several probability curves (e.g., 5 probability curves corresponding to the Big Five) for any number of answered questions. In the initial state where the questions have not yet been answered, all missing question indicators can have a value of "missing" (e.g., 0). Each time a question is answered, an update of the output value is calculated, and as the number of answered questions increases, the width of the probability curve of the output layer decreases, and the accuracy with which the neural network 602 calculates the result steadily improves.

[0041] Such a structure of the neural network 602 is particularly advantageous because it allows the user to repeatedly select the next question to be answered from the complete set of questions, where in each repetition, the next question can be selected according to the user's answer to the previous question, and where in each repetition, the next question can be selected as one of the questions in the complete set of questions that is determined to have the most influence on the achievable results for calculating the user's personality data. For this purpose, for each answered question, several (e.g., five) probability curves can be recalculated, and among the recalculated probability curves, the one with the largest width (i.e., representing the probability curve with the currently lowest accuracy) can be determined. As the next question in the repetition, the question of this dimension can be selected to improve the accuracy of this dimension. To determine the most influential question, the degree to which a change in the digital score input to each of the input nodes changes the probability curve (e.g., the degree to which the width of the curve changes) can be determined for each of the input nodes of the neural network 602. Based on this, the question associated with the input node determined to have the highest degree of change in the probability curve can be selected as the most influential question in each repetition.

[0042] The advanced structure of the neural network 602 can also be advantageous for enabling easy integration of feedback into the neural network. As described above, when the feedback represents new input values that have not yet been input into the neural network 602, when training the neural network 602, new input nodes can simply be added to the neural network 602, and the new input values are assigned to the new input nodes. In this way, any kind of new feedback can be easily integrated into the network, and the neural network 602 can improve its ability to calculate personality data. As an implementation to reduce the computational complexity when adding new input nodes, when the network is trained to correlate new input nodes with other nodes in the network, it is conceivable to incorporate only those nodes that are determined to be the most influential with respect to the optimally achievable results into the calculation, thereby avoiding incorporating all nodes into the calculation. Also, when the network is trained to correlate new input nodes with other nodes in the network, for example, it is conceivable to limit the number of layers to be calculated in advance (e.g., to 2 or 3) so as not to calculate all subsequent combinations of nodes.

[0043] In the above description, the technology for efficiently obtaining the digital representation of the user's personality data was exemplified in the context of adapting the driving settings of the vehicle, such as the fuel and brake response behavior of the vehicle, to the user's personality. In this case, the method described in this specification can also be shown as a method for adapting the driving settings of the vehicle, including efficiently obtaining the digital representation of the user's personality data. Adapting the fuel and brake response behavior of the vehicle is only an example of adapting the driving settings of the vehicle. More generally, it will be understood that adapting the driving settings of the vehicle may include adapting any vehicle settings that affect the driving behavior of the vehicle. Adapting the driving settings of the vehicle may include at least one of adapting the fuel and brake response behavior of the vehicle, adapting the chassis settings of the vehicle, adapting the drive mode of the vehicle, and adapting the settings of the adaptive cruise control (ACC) of the vehicle, etc., to the user's personality. Adapting the drive mode of the vehicle may include setting the economy, comfort, or sport mode that affects the accelerator pedal and fuel consumption behavior of the vehicle according to the personality of the driver. For example, when the personality data indicates that the driver tends to avoid risks, the drive mode can be set to economy or comfort. On the other hand, for a driver who tends to have a personality seeking risks, the drive mode can be set to sport mode. Adapting the drive mode of the vehicle may also include, for example, enabling / disabling the automatic four-wheel drive (4WD) mode of the vehicle. Adapting the settings of the ACC may include, for example, setting the distance to the vehicle ahead and / or the target driving speed according to the risk aversion of the driver.

[0044] The technology presented in this specification can also be used for other purposes in the context of a vehicle, such as adapting the environmental conditions in the passenger compartment of the vehicle (or more generally, for other means of transportation such as aircraft, trains, etc., adapting the environmental conditions in the passenger compartment). In this case, the method presented in this specification can also be shown as a method for adapting the environmental conditions in the passenger compartment of a means of transportation, including efficiently obtaining a digital representation of the user's personality data. Adapting the environmental conditions in the passenger compartment of a means of transportation can include adapting the temperature of the passenger compartment (e.g., by adapting the air conditioning settings of the passenger compartment), adapting the internal lighting of the passenger compartment, and adjusting at least one of the oxygen levels in the passenger compartment, etc., to fit the user's personality. In addition to, or instead of, adapting the environmental conditions in the passenger compartment, the technology presented in this specification can also be used to adapt user-specific settings related to the passenger compartment. Adapting user-specific settings related to the passenger compartment of a means of transportation can include adapting the seat settings (e.g., seat height, seat position, seat massage settings, seat belt tension, etc.) to the user in the passenger compartment, and adapting at least one of the equalizer settings of the sound system provided to the user in the passenger compartment (e.g., increasing or decreasing bass or treble) to the user's personality.

[0045] Any of the above-mentioned conformances of the vehicle / transportation means settings can be carried out in addition to conforming to the user's personality, taking into account (or "based on" / "according to") the user's sensor data indicating the attention level of the user obtained in the passenger compartment. In other words, the client device can be set to adapt at least one of the driving settings of the vehicle, the environmental conditions in the passenger compartment, and the user-specific settings related to the passenger compartment, considering not only the digital representation of the user's personality data but also the sensor data indicating the attention level of the user. That is, the digital representation of the user's personality data and the sensor data indicating the attention level of the user can be combined before performing the above-mentioned conformance. The sensor data indicating the attention level of the user can include, for example, data related to at least one of the user's heart rate, respiration, fatigue, reaction time, and alcohol / drug level. The sensor data is collected, for example, by at least one sensor installed in the passenger compartment or the user's mobile terminal.

[0046] FIG. 7 shows an exemplary implementation that includes considering a driver's attention level in combination with the driver's personality data to adapt the driving settings of a vehicle, the environmental conditions in the passenger compartment, and / or user-specific settings related to the passenger compartment. The driver's attention level is checked by corresponding sensors, for example, with respect to the user's reaction time, drowsiness, heart rate, breathing, alcohol / drug level, or abnormal behavior of the user. In the left portion of the figure, since the collected sensor data indicates the user's normal attention level, vehicle settings including, for example, speed, volume, temperature, seat settings, etc. can remain at the normal level (e.g., be adapted to the driver's personality, i.e., "MindDNA"). In the central portion of the figure, since the sensor data indicates a decrease in the driver's attention level, vehicle settings can be changed, including turning on the seat massage function, decelerating, increasing the volume, lowering the temperature setting, etc., in order to refresh the driver's attention again. Optionally, an attention test can be performed, for example, by asking the driver to provide a voice-based response in a question / answer scheme, and the results of the attention test can be considered when adapting the above settings. On the other hand, in the right portion of the figure, since the sensor data indicates that the driver's attention level is very low, a warning can be issued to the user and the vehicle settings can be adapted accordingly, for example, driving at a very low speed (and, for example, forcing the vehicle to stop at the next stop opportunity), muting the sound, and / or guiding the user to the next hotel by the navigation system, etc.

[0047] As described above (e.g., by adapting at least one of the driving settings of the vehicle, the environmental conditions in the passenger compartment, and the user-specific settings related to the passenger compartment), in order to provide a user-adapted service to the user, the client device can further consider body scan data indicating the user's (e.g., physical) characteristics that are derivable by scanning the user's body (e.g., at least in part) before providing the user-adapted service to the user (e.g., before the user drives the vehicle). User characteristics derivable by scanning the user's body can include, for example, at least one of the user's size, weight, gender, age, height, posture, and emotional state. The body scan data is obtained by acquiring one or more images or audio signals of the user by a camera or a voice recorder (e.g., of the user's mobile terminal or installed in the vehicle / transportation means), where body / face / voice recognition technology can be used to scan the user's body to derive the above-described user characteristics. Thus, the client device is configured to provide a user-adapted service by considering not only the digital representation of the user's personality data but also the body scan data (or "based on" / "therefore"). That is, the digital representation of the user's personality data and the body scan data can be combined before providing the user-adapted service to the user. FIG. 8 shows an exemplary implementation that includes considering the driver's body scan data (e.g., acquired by the driver's mobile terminal such as a smartphone, smartwatch, fitness tracker, etc. before entering the vehicle) in combination with the driver's personality data to appropriately adapt the driving settings of the vehicle, the environmental conditions in the passenger compartment, and / or the user-specific settings related to the passenger compartment. In this figure, the body scan data is denoted as "BodyDNA" and is combined with "MindDNA" to form the so-called "LifeDNA". It will also be understood that the acquired body scan data can also be used, as described above, to provide feedback for characterizing the user in order to update the neural network.

[0048] In other vehicle-related use cases, using the technology presented in this specification, it is also possible to determine vehicle settings that conform to the user's personality before manufacturing a vehicle, and the vehicle can be manufactured based on (or "according to") the determined vehicle settings. The vehicle can be manufactured with different setting options such as different motor options each having a different motor output, drive technology options (e.g., support for two-wheel drive (2WD) or 4WD technology), chassis options, different drive mode options, support for ACC, etc. (e.g., provided by the vehicle manufacturer). When a new vehicle is manufactured for the user, the vehicle settings are determined to specifically conform to the user's personality. For example, if the personality data indicates that the user has a tendency to avoid risks, the determined vehicle settings may include the selection of a motor with a lower output compared to the vehicle settings determined for a user whose personality data indicates a preference for taking risks. Based on the determined vehicle settings, the vehicle can be manufactured as appropriate. Therefore, in line with the above description, it is also possible to envision a method of vehicle manufacturing that includes efficiently obtaining a digital representation of the user's personality data from the server by the client device, and the digital representation of the personality data is processed at the client device to provide vehicle settings that conform to the user's personality. This method includes sending a request for the digital representation of the user's personality data from the client device to the server, and the client device receiving from the server the requested digital representation of the user's personality data, where the digital representation of the user's personality data is calculated using a neural network trained to calculate the user's personality data based on the input obtained from the user, processing the digital representation of the personality data to determine vehicle settings that conform to the user's personality, and manufacturing a vehicle based on the determined vehicle settings. It will be understood that in the vehicle manufacturing process, the determined vehicle settings can also affect the manufacturing of the vehicle parts required for vehicle manufacturing.For example, the manufacture of a vehicle can include the manufacture of one or more vehicle parts used in the manufacture of the vehicle, and the vehicle parts are manufactured according to the determined vehicle settings (e.g., using a 3D printer).

[0049] It will be understood that the techniques presented herein are applicable not only to use cases related to vehicles / transportation means, but also to other use cases such as, for example, adapting the settings of smart home appliances or robots to the user's personality. Thus, in accordance with the above description, a method of adapting the settings of smart home appliances (e.g., automatic roller shutters, air conditioners, refrigerators, washing machines, televisions, set-top boxes, etc.) including efficiently obtaining a digital representation of the user's personality data can also be envisioned, where the digital representation of the user's personality data is processed in a client device to adapt the way the smart home appliance performs main tasks such as, for example, shutter (roller shutter), heating / cooling (air conditioner), refrigeration (refrigerator), washing (washing machine) or recording / display (television / set-top box) tasks. Similarly, in accordance with the above description, a method of adapting the settings of a robot (e.g., a humanoid robot or a domestic robot configured to perform one or more household tasks) including efficiently obtaining a digital representation of the user's personality data can be envisioned, where the digital representation of the user's personality is processed in a client device to adapt the way the robot performs tasks (e.g., to adapt the way a domestic robot performs household chores) to the user's personality.

[0050] Various other usage examples are generally conceivable. Other usage examples may include, for example, adapting the settings of a virtual robot, adapting the settings of a medical device, or even stimulating the brain. Thus, in line with the above description, it is also possible to envision a method for adapting the settings of a virtual robot (such as a chatbot, virtual service staff, virtual personal assistant) that includes efficiently obtaining a digital representation of the user's personality data, where the digital representation of the user's personality is processed on a client device in order to adapt the settings of the virtual robot to the user's personality (for example, to adapt the way the virtual robot performs tasks to support the user). Similarly, in line with the above description, it is possible to envision a method for adapting the settings of a medical device (such as a bedside medical device) that includes efficiently obtaining a digital representation of the user's personality data, where the digital representation of the user's personality is processed on a client device in order to adapt the settings of the medical device to the user's personality (for example, to adapt the administration plan such as the dosage of analgesics). Furthermore, it is possible to envision a method for stimulating the brain (such as a biological or virtual representation of the brain) that includes efficiently obtaining a digital representation of the user's personality data, where the digital representation of the personality is processed on the user's client device in order to adapt the brain stimulation procedure based on the user's personality. The stimulation procedure may include, for example, electrical stimulation of a biological brain or adapting / resetting a virtual representation of the brain. The virtual representation of the brain is supplied to a robot or other form of intelligent system, for example, to affect the behavior of such a system based on the user's personality.

[0051] In all of the above examples and use cases, when referring to "adapting" a configuration or setting "to the user's personality", such adaptation can be implemented using a pre - defined mapping that maps certain characteristics of the user's personality (represented by the digital representation of the user's personality data) to a specific configuration or setting of the corresponding device / apparatus (e.g., a vehicle, means of transportation, smart home appliance, robot, medical device, etc. as described above). As described above, for example, if the personality data indicates that the driver has a tendency to avoid risks, the drive mode of the vehicle can be set to economy or comfort, while for a driver who tends to have a personality seeking risks, the drive mode can be set to sports mode. Such a mapping can be defined in advance for each possible combination of personality characteristics - configuration / settings, and the configuration or setting of the device / apparatus can be appropriately adapted according to the obtained user's personality data. The user's personality characteristics can, for example, correspond to the values of the personality dimensions (e.g., from the Big Five) output by a neural network as described above.

[0052] The advantages of the technology presented in this specification are fully understood from the above description, and it will be apparent that various changes can be made to the form, structure, and arrangement of its exemplary embodiments without departing from the scope of the present disclosure or sacrificing all of its advantageous effects. Since the technology presented in this specification can be modified in many ways, it will be understood that the present disclosure should be limited only by the following claims. Some or all of the above - described embodiments can also be described as follows, but are not limited thereto. (Appendix 1) A method for enabling a digital representation of a user's (402) personality data to be efficiently obtained from a server (404) by a client device (502, 406), wherein the digital representation of the personality data is processed in the client device (406) to provide a user-adapted service to the user (402), the method being executed by the server (404), Storing (S202) a neural network (602) trained to calculate the user's (402) personality data based on input obtained from the user (402); Receiving (S204) a request for a digital representation of the user's (402) personality data from the client device (502, 406); Transmitting (S206) the requested digital representation of the user's (402) personality data to the client device (502, 406), wherein the user's (402) personality data is calculated using the neural network (602) based on input obtained from the user (402); A method having the above. (Appendix 2) The digital representation of the user's (402) personality data is processed in the client device (502, 406) to configure at least one device (406) that provides a service to the user (402), Optionally, the at least one device (406) includes the client device (406). The method according to Appendix 1. (Appendix 3) Receiving feedback characterizing the user (402); Updating the neural network (602) based on the feedback; Transmitting a digital representation of the updated personality data of the user (402) to the client device (502, 406), wherein the updated personality data of the user (402) is calculated using the updated neural network (602). Optionally, the digital representation of the updated personality data of the user (402) is processed in the client device (502, 406) to improve the settings of the at least one device (406) that provides the service to the user (402). The method according to appendix 1 or 2. (Appendix 4) The feedback includes behavior data reflecting the behavior of the user (402) monitored in the at least one device (406) when using the service provided by the at least one device (406). Optionally, the behavior data is monitored using measurements performed by the at least one device (406) that provides the service to the user (402). The method according to appendix 3. (Appendix 5) The at least one device (406) includes a vehicle, and the behavior data includes data reflecting the driving behavior of the user (402). The method according to appendix 4. (Appendix 6) The personality data of the user (402) is calculated before receiving the request from the client device (502, 406), and the request includes an access code pre-provided to the user (402) by the server (404) when calculating the personality data of the user (402), and the access code enables the user (402) to access the digital representation of the personality data of the user (402) from other client devices (502, 406). The method according to any one of appendices 1 to 5. (Appendix 7) The input obtained from the user corresponds to a digital score reflecting an answer to a question regarding at least one of the personality, goals, and motivations of the user (402), and each of the digital scores is used as an input to a separate input node of the neural network (602) when calculating the personality data of the user (402) using the neural network (602). The method according to any one of Appendices 1 to 6. (Appendix 8) The question corresponds to a question selected from a set of questions representing results that can optimally achieve calculating the personality data of the user (402), The selected question corresponds to a question in the set of questions determined to be most influential regarding the optimally achievable result, Optionally, the number of the selected questions is less than 10% of the number of questions included in the set of questions. The method according to Appendix 7. (Appendix 9) The question is selected from the set of questions based on correlating the result achievable by each single question in the set of questions with the optimally achievable result and selecting a question from the set of questions having the highest correlation with the optimally achievable result, or, The question is repeatedly selected from the set of questions, and in each repetition, the next question is selected according to the user's answer to the previous question, and in each repetition, the next question is selected as one question in the set of questions determined to be most influential for the result achievable for calculating the personality data of the user. Optionally, the neural network (602) includes a plurality of output nodes representing a probability curve (604) of the result of the personality data of the user (402), and determining the most influential question of the question set as each of the next questions of the iteration includes, for each of the input nodes of the neural network (602), determining the degree to which a change in the digital score input to each of the input nodes of the neural network (602) changes the probability curve (604). The method according to appendix 8. (Appendix 10) A method for enabling a digital representation of personality data of a user (402) to be efficiently obtained from a server (404) by a client device (502, 406), the method being executed by the client device (502, 406), Sending a request for a digital representation of the personality data of the user (402) to the server (404) (S302); Receiving the requested digital representation of the personality data of the user (402) from the server (404) (S304), wherein the personality data of the user (402) is calculated using a neural network (602) trained to calculate the personality data of the user (402) based on an input obtained from the user (402), based on the input obtained from the user (402). Processing (S306) the digital representation of the personality data to provide a user-adapted service to the user (402); A method having. (Appendix 11) A computer program product, including a program code portion for executing the method according to any one of appendices 1 to 10 when the computer program product is executed on one or more computing units. (Appendix 12) A computer program product according to appended claim 11, stored on one or more computer-readable recording media. (Appended claim 13) The server (100, 404) that enables a digital representation of user (402)'s personality data to be efficiently acquired from a server (404) by a client device (502, 406), wherein the digital representation of the personality data is processed in the client device (502, 406) to provide a user-adapted service to the user (402), the server (404) includes at least one processor (102) and at least one memory (104), and the at least one memory (104) includes instructions executable by the at least one processor (102) such that the server (404) is operable to execute the method according to any one of appended claims 1 to 9. (Appended claim 14) The client device (110, 502, 406) that enables a digital representation of user (402)'s personality data to be efficiently acquired from a server (404), the client device (110, 502, 406) includes at least one processor (112) and at least one memory (114), and the at least one memory (114) includes instructions executable by the at least one processor (112) such that the client device (110, 502, 406) is operable to execute the method according to appended claim 10. (Appended claim 15) A system including the server (100, 404) according to appended claim 13 and at least one client device (110, 502, 406) according to appended claim 14.

Claims

1. 1. A method comprising: obtaining, by a client device (110) from a server (100) a digital representation of a user's personality data, the digital representation of the personality data being processed at the client device (110) to provide a vehicle setting adapted to the personality of the user, the method being executed by the server (100); storing (S202) a neural network (602) trained to calculate personality data of a user based on inputs obtained from the user; receiving (S204) a request from the client device (110) for a digital representation of a user's personality data; transmitting (S206) the requested digital representation of the user's personality data to the client device (110), wherein the personality data of the user is calculated using the neural network (602) based on input obtained from the user; the digital representation of the user's personality data is processed at the client device (110) prior to manufacturing a vehicle to determine a vehicle configuration for the manufactured vehicle, the vehicle being capable of being manufactured with different configuration options, the determined vehicle configuration being adapted to the personality of the user; The method comprises: receiving feedback characterizing the user; updating the neural network (602) based on the feedback; and The method further comprises transmitting a digital representation of the user's updated personality data to the client device (110), wherein the updated personality data of the user is calculated using the updated neural network (602).

2. The vehicle is manufactured based on the determined vehicle settings. The method of claim 1.

3. The method of claim 2 , wherein manufacturing the vehicle includes manufacturing one or more vehicle parts used to manufacture the vehicle, the vehicle parts being manufactured according to the determined vehicle configuration.

4. The digital representation of the updated personality data of the user is processed at the client device (110) to improve the vehicle configuration.

4. The method according to any one of claims 1 to 3.

5. The feedback is collected at the client device (110).

5. The method according to any one of claims 1 to 4.

6. the feedback being indicative of the personality of the user; 6. The method according to any one of claims 1 to 5.

7. The personality data of the user is Psychological characteristics of the user; the user's preferences, Indicate at least one of:

7. The method according to any one of claims 1 to 6.

8. the inputs obtained from the user correspond to digital scores reflecting answers to questions relating to at least one of the user's personality, goals, and motivations, each of the digital scores being used as an input to a separate input node of the neural network (602) when using the neural network (602) to calculate the personality data for the user; 8. The method according to any one of claims 1 to 7.

9. The questions regarding the personality of the user include: International Personality Item Pool (IPIP), HEXACO-60 pool, Big-Five-Inventory-10 (BFI-10) pool, Questions about psychological characteristics of the user; Questions about the user's preferences; Responding to at least one of the questions: The method according to claim 8.

10. the questions correspond to questions selected from a set of questions representing optimally achievable results for computing personality data of the user; the selected questions correspond to questions from the question set that are determined to be most influential with respect to the optimally achievable outcome.

10. The method according to claim 8 or 9.

11. the number of selected questions is less than 10% of the number of questions included in the question set; The method of claim 10.

12. the questions are selected from the set of questions based on correlating the outcome achievable by each single question in the set of questions with the optimally achievable outcome and selecting the question from the set of questions having the highest correlation with the optimally achievable outcome.

12. The method according to claim 10 or 11.

13. the questions are selected iteratively from the set of questions, and in each iteration a next question is selected depending on the user's answer to a previous question, and in each iteration the next question is selected as the one question from the set of questions that is determined to be most influential on the achievable results for calculating the personality data of the user.

12. The method according to claim 10 or 11.

14. the neural network (602) includes a plurality of output nodes representing a probability curve (604) of outcomes of the personality data of the user, and determining a most influential question of the question set as the next question for each of the iterations includes determining, for each of the input nodes of the neural network (602), an extent to which a change in the digital score input to each of the input nodes of the neural network (602) changes the probability curve (604); The method of claim 13.

15. the personality data of the user is calculated prior to receiving the request from the client device (110), the request including an access code previously provided to the user by the server (100) when calculating the personality data of the user, the access code enabling the user to access the digital representation of the personality data of the user from another client device (110); 15. The method according to any one of claims 1 to 14.

16. 1. A method comprising: obtaining, by a client device (110) from a server (100) a digital representation of a user's personality data, said method being executed by said client device (110); Sending (S302) a request for a digital representation of a user's personality data to said server (100); receiving (S304) the requested digital representation of the user's personality data from the server (100), wherein the personality data of the user is calculated based on inputs obtained from the user using a neural network (602) trained to calculate the user's personality data based on inputs obtained from the user; processing (S306) the digital representation of the personality data to determine a vehicle configuration for the manufactured vehicle prior to manufacturing the vehicle, the vehicle being capable of being manufactured with different configuration options, the determined vehicle configuration being adapted to the personality of the user; The method comprises: sending feedback characterizing said user to said server (100); The method further comprises receiving from the server (100) a digital representation of updated personality data of the user, the updated personality data of the user being calculated using the neural network (602) that is updated based on the feedback.

17. The vehicle is manufactured based on the determined vehicle settings.

17. The method of claim 16.

18. 20. The method of claim 17, wherein manufacturing the vehicle includes manufacturing one or more vehicle parts used to manufacture the vehicle, the vehicle parts being manufactured according to the determined vehicle configuration.

19. The digital representation of the updated personality data of the user is processed at the client device (110) to improve the vehicle configuration.

19. The method according to any one of claims 16 to 18.

20. The feedback is collected at the client device (110).

20. The method according to any one of claims 16 to 19.

21. the feedback being indicative of the personality of the user; 21. The method according to any one of claims 16 to 20.

22. The personality data of the user is Psychological characteristics of the user; the user's preferences, Indicate at least one of:

22. The method according to any one of claims 16 to 21.

23. the inputs obtained from the user correspond to digital scores reflecting answers to questions relating to at least one of the user's personality, goals, and motivations, each of the digital scores being used as an input to a separate input node of the neural network (602) when using the neural network (602) to calculate the personality data for the user; 23. The method according to any one of claims 16 to 22.

24. The questions regarding the personality of the user include: International Personality Item Pool (IPIP), HEXACO-60 pool, Big-Five-Inventory-10 (BFI-10) pool, Questions about psychological characteristics of the user; Questions about the user's preferences; Responding to at least one of the questions:

24. The method of claim 23.

25. the questions correspond to questions selected from a set of questions representing optimally achievable results for computing personality data of the user; the selected questions correspond to questions from the question set that are determined to be most influential with respect to the optimally achievable outcome.

25. The method of claim 23 or 24.

26. the number of selected questions is less than 10% of the number of questions included in the question set; 26. The method of claim 25.

27. the questions are selected from the set of questions based on correlating outcomes achievable by each single question in the set of questions with the optimally achievable outcomes and selecting the questions from the set of questions having the highest correlation with the optimally achievable outcomes.

27. The method of claim 25 or 26.

28. the questions are selected iteratively from the set of questions, and in each iteration a next question is selected depending on the user's answer to a previous question, and in each iteration the next question is selected as the one question from the set of questions that is determined to be most influential on the achievable results for calculating the personality data of the user.

27. The method of claim 25 or 26.

29. the neural network (602) includes a plurality of output nodes representing a probability curve (604) of outcomes of the personality data of the user, and determining a most influential question of the question set as the next question for each of the iterations includes determining, for each of the input nodes of the neural network (602), an extent to which a change in a digital score input to each of the input nodes of the neural network (602) changes the probability curve (604); 29. The method of claim 28.

30. the personality data of the user is calculated prior to sending the request to the server (100), the request including an access code previously provided to the user by the server (100) when calculating the personality data of the user, the access code enabling the user to access the digital representation of the personality data of the user from another client device (110); 30. The method of any one of claims 16 to 29.

31. A computer program comprising program code portions which, when executed on one or more computing units, cause the one or more computing units to perform a method according to any one of claims 1 to 30.

32. 32. A computer program according to claim 31 stored on one or more computer readable recording media.

33. A server (100) that enables a digital representation of a user's personality data to be obtained from the server (100) by a client device (110), the digital representation of the personality data being processed in the client device (110) to provide a vehicle setting that is adapted to the personality of the user, the server (100) comprising at least one processor (102) and at least one memory (104), the at least one memory (104) including instructions executable by the at least one processor (102) such that the server (100) is operable to perform the method of any one of claims 1 to 15.

34. A client device (110) that enables a digital representation of a user's personality data to be obtained from a server (100), the client device (110) comprising at least one processor (112) and at least one memory (114), the at least one memory (114) including instructions executable by the at least one processor (112) such that the client device (110) is operable to perform the method of any one of claims 16 to 30.

35. A system comprising a server (100) according to claim 33 and at least one client device (110) according to claim 34.

36. 1. A method comprising: obtaining a digital representation of personality data of a user, the digital representation of the personality data being processed to provide a vehicle configuration that is adapted to the personality of the user, the method being performed by one or more computing devices; obtaining a digital representation of a user's personality data, the personality data of the user being calculated based on inputs obtained from the user using a neural network (602) trained to calculate the user's personality data based on inputs obtained from the user; processing the digital representation of the personality data to determine a vehicle configuration for the manufactured vehicle prior to manufacturing the vehicle, the vehicle being capable of being manufactured with different configuration options, the determined vehicle configuration being adapted to the personality of the user; The method comprises: obtaining feedback characterizing the user; and obtaining a digital representation of updated personality data of the user, the updated personality data of the user being calculated using the neural network (602) that is updated based on the feedback.

37. 37. The method of claim 36, wherein the vehicle is manufactured based on the determined vehicle settings.

Images